Development and Validation of a Nomogram for Individually Predicting Pathologic Complete Remission After Preoperative Chemotherapy in Chinese Breast Cancer: A Population-Based Study

Integrative analysis identifies molecular features of fibroblast and the significance of fibrosis on neoadjuvant chemotherapy response in breast cancer

BACKGROUND

The molecular features of fibroblasts and the role of fibrosis in neoadjuvant chemotherapy (NAC) response and breast cancer (BRCA) prognosis remain unclear. Therefore, this study aimed to investigate the impact of interstitial fibrosis on the response and prognosis of patients with BRCA undergoing NAC treatment.

MATERIALS AND METHODS

The molecular characteristics of pathologic complete response (pCR) and non-pCR (npCR) in patients with BRCA were analyzed using multiomics analysis. A clinical cohort was collected to investigate the predictive value of fibrosis in patients with BRCA.

RESULTS

Fibrosis-related signaling pathways were significantly upregulated in patients with npCR. npCR may be associated with distinct and highly active fibroblast subtypes. Patients with high fibrosis had lower pCR rates. The fibrosis-dependent nomogram for pCR showed efficient predictive ability [training set: area under the curve [AUC]=0.871, validation set: AUC=0.792]. Patients with low fibrosis had a significantly better prognosis than those with high fibrosis, and those with a high fibrotic focus index had significantly shorter overall and recurrence-free survival. Therefore, fibrosis can be used to predict pCR. Our findings provide a basis for decision-making in the treatment of BRCA.

CONCLUSIONS

npCR is associated with a distinct and highly active fibroblast subtype. Furthermore, patients with high fibrosis have lower pCR rates and shorter long-term survival. Therefore, fibrosis can predict pCR. A nomogram that includes fibrosis can provide a basis for decision-making in the treatment of BRCA.

Systematic Review of Nomograms Used for Predicting Pathological Complete Response in Early Breast Cancer

Pathological complete response (pCR) is an important surrogate outcome to assess the effects of neoadjuvant chemotherapy (NAC). Nomograms to predict pCR have been developed with local data to better select patients who are likely to benefit from NAC; however, they were never critically reviewed regarding their internal and external validity. The purpose of this systematic review was to critically appraise nomograms published in the last 20 years (2010-2022). Articles about nomograms were searched in databases, such as PubMed/MEDLINE, Embase and Cochrane. A total of 1120 hits were found, and seven studies were included for analyses. No meta-analysis could be performed due to heterogeneous reports on outcomes, including the definition of pCR and subtypes. Most nomograms were developed in Asian centers, and nonrandomized retrospective cohorts were the most common sources of data. The most common subtype included in the studies was triple negative (50%). There were articles that included HER2+ (>80%). In one study, scholars performed additional validation of the nomogram using DFS and OS as outcomes; however, there was a lack of clarity on how such endpoints were measured. Nomograms to predict pCR cannot be extrapolated to other settings due to local preferences/availability of NAC. The main gaps identified in this review are also opportunities for future nomogram research and development.

A nomogram based on platelet-to-lymphocyte ratio for predicting pathological complete response of breast cancer after neoadjuvant chemotherapy

OBJECTIVE

To investigate the role of platelet-to-lymphocyte ratio (PLR) in complete pathological response (pCR) of breast cancer (BC) patients after neoadjuvant chemotherapy (NAC), as well as to establish and validate a nomogram for predicting pCR.

METHODS

BC patients diagnosed and treated in the First Affiliated Hospital of Xi'an Jiaotong University from January 2019 to June 2022 were included. The correlation between pCR and clinicopathological characteristics was analyzed by Chi-square test. Logistic regression analysis was performed to evaluate the factors that might affect pCR. Based on the results of regression analysis, a nomogram for predicting pCR was established and validated.

RESULTS

A total of 112 BC patients were included in this study. 50.89% of the patients acquired pCR after NAC. Chi-square test showed that PLR was significantly correlated with pCR (X2 = 18.878, P < 0.001). And the PLR before NAC in pCR group was lower than that in Non-pCR group (t = 3.290, P = 0.001). Logistic regression analysis suggested that white blood cell (WBC) [odds ratio (OR): 0.19, 95% confidence interval (CI): 0.04-0.85, P = 0.030)], platelet (PLT) (OR: 0.19, 95%CI: 0.04-0.85, P = 0.030), PLR (OR: 0.18, 95%CI: 0.04-0.90, P = 0.036) and tumor grade (OR: 9.24, 95%CI: 1.89-45.07, P = 0.006) were independent predictors of pCR after NAC. A nomogram prediction model based on WBC, PLR, PLR and tumor grade showed a good predictive ability.

CONCLUSION

PLR, PLT, WBC and tumor grade were independent predictors of pCR in BC patients after NAC. The nomogram based on the above positive factors showed a good predictive ability.

Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy

Background

Given the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features.

Methods

Clinicopathological data from stage I-III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated.

Results

In the training set, data from 539 patients were gathered: pCR rate was 11.3% [95% confidence interval (CI): 8.6-13.9] (luminal A-like: 5.3%, 95% CI: 1.5-9.1, and luminal B-like: 13.1%, 95% CI: 9.8-13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC): 0.69; p < 0.001). Clinical stage I-II (OR: 3.67, 95% CI: 1.75-7.71, p = 0.001), Ki67 ⩾44% (OR: 3.00, 95% CI: 1.59-5.65, p = 0.001), and progesterone receptor (PR) <1% (OR: 2.49, 95% CI: 1.15-5.38, p = 0.019) were independent predictors of pCR, with high replication rates at internal validation (100%, 98%, and 87%, respectively). According to the nomogram, the probability of pCR ranged from 3.4% for clinical stage III, PR > 1%, and Ki67 <44% to 53.3% for clinical stage I-II, PR < 1%, and Ki67 ⩾44% (accuracy: AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC: 0.7; p < 0.0001).

Conclusion

The combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.

Development and Validation of a Nomogram to Predict the Probability of Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy: A Retrospective Cohort Study

Background

The methods used to predict the pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have some limitations. In this study, we aimed to develop a nomogram to predict breast cancer pCR after NAC based on convenient and economical multi-system hematological indicators and clinical characteristics.

Materials and Methods

Patients diagnosed from July 2017 to July 2019 served as the training group (N = 114), and patients diagnosed in from July 2019 to July 2021 served as the validation group (N = 102). A nomogram was developed according to eight indices, including body mass index, platelet distribution width, monocyte count, albumin, cystatin C, phosphorus, hemoglobin, and D-dimer, which were determined by multivariate logistic regression. Internal and external validation curves are used to calibrate the nomogram.

Results

The area under the receiver operating characteristic curve was 0.942 (95% confidence interval 0.892–0.992), and the concordance index indicated that the nomogram had good discrimination. The Hosmer–Lemeshow test and calibration curve showed that the model was well-calibrated.

Conclusion

The nomogram developed in this study can help clinicians accurately predict the possibility of patients achieving the pCR after NAC. This information can be used to decide the most effective treatment strategies for patients.

Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Background

A favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients.

Patients and methods

Between January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort (n = 563) and an independent cohort (n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models.

Results

Clinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively.

Conclusions

In patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials.

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Establishing the first risk stratification nomogram for BC treated with NAC and validate its performance in BC cohorts.

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Incorporating residual cancer burden index into predictive nomogram for the first time.

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Predictive model can be utilized to predict DFS for all early-stage BC treated with NAC.

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Performing a continuous rather than categorized model to predict individual survival.

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The risk stratification can be used to select comparable population in trial design.